• Title/Summary/Keyword: Bayesian information

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Bayesian estimation of the Korea professional baseball players' hitting ability based on the batting average (한국프로야구 선수들의 타율에 기반된 타격 능력의 베이지안 추정)

  • Cho, Yong Ju;Lee, Kwang Ho
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.1
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    • pp.197-207
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    • 2015
  • In baseball game, the hitting ability of batter is frequently assessed by a batting average, a run batted in, a home run, a run scored, an on-base percentage, etc. Recently, more comprehensive indicators such as OPS, ISO, SECA, TA, RC and XR are often used. But, these measures generally shows large deviations since they are calculated from the data for a certain period of time, and they are not an estimate of a population parameter, either. In this paper, we will presume the pure hitting ability of the korea professional baseball players as a parameter which is depend upon at bat. We will estimate the parameter by using the Bayesian method.

Bayesian smoothing under structural measurement error model with multiple covariates

  • Hwang, Jinseub;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.3
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    • pp.709-720
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    • 2017
  • In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

Region Growing Based Variable Window Size Decision Algorithm for Image Denoising (영상 잡음 제거를 위한 영역 확장 기반 가변 윈도우 크기 결정 알고리즘)

  • 엄일규;김유신
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.5
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    • pp.111-116
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    • 2004
  • It is essential to know the information about the prior model for wavelet coefficients, the probability distribution of noise, and the variance of wavelet coefficients for noise reduction using Bayesian estimation in wavelet domain. In general denoising methods, the signal variance is estimated from the proper prior model for wavelet coefficients. In this paper, we propose a variable window size decision algorithm to estimate signal variance according to image region. Simulation results shows the proposed method have better PSNRs than those of the state of art denoising methods.

LANDSLIDE SUSCEPTIBILITY MAPPING AND VERIFICATION USING THE GIS AND BAYESIAN PROBABILITY MODEL IN BOEUN, KOREA

  • Choi, Jae-Won;Lee, Sa-Ro;Yu, Young-Tae
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • 2003.04a
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    • pp.100-100
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    • 2003
  • The purpose of this study is to reveals spatial relationships between landslides and geospatial data set, map the landslide susceptibility using the relationships and verify the landslide susceptibility using the landslide occurrence data in Bosun area in 1998. Landslide locations were detected from aerial photography and field survey and topography, soil, forest, and land use data sets were constructed as a spatial database using GIS. As the landslide occurrence factors, slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood and land use were used. Is extract the relationship between landslides and geospatial database, Bayesian probability methods, likelihood ratio and weight of evidence, were applied and the ratio and contrast value that is W$\^$+/- W$\^$-/ were calculated. The landslide susceptibility index was calculated by summation of the likelihood ratio and contrast value and the landslide susceptibility maps were generated using the index. As a result, it is expected that spatial relationships between landslides and geospatial database is helpful to explain the characteristics of landslide and the landslide susceptibility map is used to reduce associated hazards, and to plan land use and construction.

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Self-Organizing Map for Blind Channel Equalization

  • Han, Soo-Whan
    • Journal of information and communication convergence engineering
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    • v.8 no.6
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    • pp.609-617
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    • 2010
  • This paper is concerned with the use of a selforganizing map (SOM) to estimate the desired channel states of an unknown digital communication channel for blind equalization. The modification of SOM is accomplished by using the Bayesian likelihood fitness function and the relation between the desired channel states and channel output states. At the end of each clustering epoch, a set of estimated clusters for an unknown channel is chosen as a set of pre-defined desired channel states, and used to extract the channel output states. Next, all of the possible desired channel states are constructed by considering the combinations of extracted channel output states, and a set of the desired states characterized by the maximal value of the Bayesian fitness is subsequently selected for the next SOM clustering epoch. This modification of SOM makes it possible to search the optimal desired channel states of an unknown channel. In simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The performance of the proposed method is compared with those of the "conventional" SOM and an existing hybrid genetic algorithm. Relatively high accuracy and fast search speed have been achieved by using the proposed method.

Odds ratio of major risk factors associated with delirium by Bayesian network (베이지안 네트워크를 활용한 정신장애 질병 섬망의 주요 위험인자와 오즈비)

  • Lee, Jea-Young;Choi, Young-Jin
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.2
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    • pp.217-225
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    • 2011
  • It is important to find risk factors associated with mental disorder. Also the hazard ratio that represent the relationship of risk factors with illness is main interest in medicine. Thus we used odds ratio to explore the relationship between mental disorder and risk factors. On this paper, when we applied Bayesian network to delirium of mental disorder, we selected major risk factors and calculated odds ratio. Especially we identified odds ratio of single risk factors and multiple risk factors.

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

  • Oh, Sunghee;Kim, Chul Soo
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.1
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    • pp.225-243
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    • 2016
  • Thanks to recent advance of next generation sequencing techniques, RNA-seq enabled to have an unprecedented opportunity to identify transcript variants with isoform diversity and allelic imbalance (Anders et al., 2012) by different transcriptional rates. To date, it is well known that those features might be associated with the aberrant patterns of disease complexity such as tissue (Anders and Huber, 2010; Anders et al., 2012; Nariai et al., 2014) specific differential expression at isoform levels or tissue specific allelic imbalance in mal-functionality of disease processes, etc. Nevertheless, the knowledge of post-transcriptional modification and AI in transcriptomic and genomic areas has been little known in the traditional platforms due to the limitation of technology and insufficient resolution. We here stress the potential of isoform variability and allelic specific expression that are relevant to the abnormality of disease mechanisms in transcriptional genetic regulatory networks. In addition, we systematically review how robust Bayesian approaches in RNA-seq have been developed and utilized in this regard in the field.

Bayesian parameter estimation and prediction in NHPP software reliability growth model (NHPP소프트웨어 신뢰도 성장모형에서 베이지안 모수추정과 예측)

  • Chang, Inhong;Jung, Deokhwan;Lee, Seungwoo;Song, Kwangyoon
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.4
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    • pp.755-762
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    • 2013
  • In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.

Bayesian analysis of directional conditionally autoregressive models (방향성 공간적 조건부 자기회귀 모형의 베이즈 분석 방법)

  • Kyung, Minjung
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.5
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    • pp.1133-1146
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    • 2016
  • Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

Evaluation of Geo-based Image Fusion on Mobile Cloud Environment using Histogram Similarity Analysis

  • Lee, Kiwon;Kang, Sanggoo
    • Korean Journal of Remote Sensing
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    • v.31 no.1
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    • pp.1-9
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    • 2015
  • Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.